GO:0090370Ontology (GO BP)GO biological process · ~19 member genes
Q-omics provides the Negative regulation of cholesterol efflux (GO:0090370) pathway profile, scoring each patient from the combined activity of its roughly 19 member genes. Pathway activity is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, the pathway is differentially active in 14, with the highest sampling consensus in KIRC. Additionally, pathway RNA activity shows 28,833 significant cross-omics associations, again with the highest sampling consensus in LGG. Together, these results highlight MESO, KIRC, and LGG as cancer lineages where the pathway shows reproducible signals across outcome, tissue activity, and molecular association analyses.
Every result is evaluated using two consensus scores. Sampling consensus measures how consistently a finding is reproduced within a cancer lineage across different conditions. Lineage consensus measures how broadly the result is shared across cancer types, distinguishing pan-cancer signals from lineage-specific patterns. Pathway-against-pathway and pathway-against-mutation comparisons are not available for ontology entities.
Survival associations
This table summarizes Negative regulation of cholesterol efflux survival associations by molecular data type. RNA-level pathway activity shows survival associations in the most cancer types (25). The rightmost column indicates the cancer type with the highest sampling consensus for each layer.
This table ranks reproducible pathway activity–survival associations across cancer types. High Negative regulation of cholesterol efflux activity shows favorable associations in MESO, LGG, THCA, COAD and SCLC, but unfavorable associations in CHOL. In the MESO Kaplan–Meier curve the low-activity group declines faster, consistent with the favorable association (log-rank p < 0.001). MESO ranks highest by sampling consensus for Negative regulation of cholesterol efflux.
This table summarizes Negative regulation of cholesterol efflux tumor–normal activity differences by data type. RNA-level activity shows significant tumor–normal differences in 14 cancer types, while mass-spec protein activity shows differences in 1. The strongest signals are in KIRC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal activity differences for the pathway. A positive fold-change indicates higher activity in tumor tissue. The pathway shows higher tumor activity across COAD and KICH and lower tumor activity in KIRC, BLCA, HNSC and KIRP. In the KIRC box plot, normal samples show higher pathway activity than tumor samples (log2 FC = −0.119, t-test p < 0.001).
This table shows molecular features associated with Negative regulation of cholesterol efflux pathway activity in patient tissues and cancer cell lines. In patient samples, pathway activity is most strongly linked to RNA and protein features, with the largest associated set in LGG. In cancer cell lines, RNA-expression features and functional dependencies dominate, with the largest set in SOFT_TISSUE.
